



'''simple docstring'''






# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig
def  lowerCamelCase__			(		_A					,     _A					,     _A=0							):
  # Format the message.
  if name is None:
    a		:   Optional[int]	  =	None
  else:
    a		:   str	  =	'.' * max(0					,     spaces - 2							) + '# {:' + str(50 - spaces							) + 's}'
    a		:   Any	  =	fmt.format(_A							)

  # Print and recurse (if needed).
  if isinstance(_A					,     _A							):
    if msg is not None:
      print(_A							)
    for k in val.keys():
      recursive_print(_A					,     val[k]					,     spaces + 2							)
  elif isinstance(_A					,     torch.Tensor							):
    print(_A					,     ':'					,     val.size()							)
  else:
    print(_A					,     ':'					,     _A							)
def  lowerCamelCase__			(		_A					,     _A					,     _A					,     _A					,     _A							):
  # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
  # for compatibility with later versions of NVIDIA Megatron-LM.
  # The inverse operation is performed inside Megatron-LM to read checkpoints:
  # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
  # If param is the weight tensor of the self-attention block, the returned tensor
  # will have to be transposed one more time to be read by HuggingFace GPT2.
  a		:   List[str]	  =	param.size()
  if checkpoint_version == 1.0:
    # version 1.0 stores [num_heads * hidden_size * num_splits, :]
    a		:   int	  =	(num_heads, hidden_size, num_splits) + input_shape[1:]
    a		:   List[str]	  =	param.view(*_A							)
    a		:   Optional[int]	  =	param.transpose(0					,     2							)
    a		:   Union[str, Any]	  =	param.transpose(1					,     2							).contiguous()
  elif checkpoint_version >= 2.0:
    # other versions store [num_heads * num_splits * hidden_size, :]
    a		:   str	  =	(num_heads, num_splits, hidden_size) + input_shape[1:]
    a		:   Any	  =	param.view(*_A							)
    a		:   Optional[Any]	  =	param.transpose(0					,     1							).contiguous()
  a		:   Tuple	  =	param.view(*_A							)
  return param
def  lowerCamelCase__			(		_A					,     _A					,     _A							):
  # The converted output model.
  a		:   Dict	  =	{}

  # old versions did not store training args
  a		:   Optional[int]	  =	input_state_dict.get('args'					,     _A							)
  if ds_args is not None:
    # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
    # from pprint import pprint
    # pprint(vars(ds_args))

    a		:   List[str]	  =	ds_args.padded_vocab_size
    a		:   Optional[Any]	  =	ds_args.max_position_embeddings
    a		:   Optional[Any]	  =	ds_args.hidden_size
    a		:   Any	  =	ds_args.num_layers
    a		:   str	  =	ds_args.num_attention_heads
    a		:   Dict	  =	ds_args.ffn_hidden_size
    # pprint(config)

  # The number of heads.
  a		:   Dict	  =	config.n_head
  # The hidden_size per head.
  a		:   Dict	  =	config.n_embd // config.n_head
  # Megatron-LM checkpoint version
  if "checkpoint_version" in input_state_dict.keys():
    a		:   int	  =	input_state_dict['checkpoint_version']
  else:
    a		:   int	  =	0.0

  # The model.
  a		:   Optional[Any]	  =	input_state_dict['model']
  # The language model.
  a		:   Any	  =	model['language_model']
  # The embeddings.
  a		:   List[str]	  =	lm['embedding']

  # The word embeddings.
  a		:   List[str]	  =	embeddings['word_embeddings']['weight']
  # Truncate the embedding table to vocab_size rows.
  a		:   List[Any]	  =	word_embeddings[: config.vocab_size, :]
  a		:   Optional[int]	  =	word_embeddings

  # The position embeddings.
  a		:   Any	  =	embeddings['position_embeddings']['weight']
  # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
  a		:   Union[str, Any]	  =	pos_embeddings.size(0							)
  if n_positions != config.n_positions:
    raise ValueError(
        f"""pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match"""							)
  # Store the position embeddings.
  a		:   Union[str, Any]	  =	pos_embeddings

  # The transformer.
  a		:   Optional[int]	  =	lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

  # The regex to extract layer names.
  a		:   Optional[Any]	  =	re.compile(r'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'							)

  # The simple map of names for "automated" rules.
  a		:   str	  =	{
      'attention.dense': '.attn.c_proj.',
      'self_attention.dense': '.attn.c_proj.',
      'mlp.dense_h_to_4h': '.mlp.c_fc.',
      'mlp.dense_4h_to_h': '.mlp.c_proj.',
  }

  # Extract the layers.
  for key, val in transformer.items():
    # Match the name.
    a		:   str	  =	layer_re.match(_A							)

    # Stop if that's not a layer
    if m is None:
      break

    # The index of the layer.
    a		:   Union[str, Any]	  =	int(m.group(1							)							)
    # The name of the operation.
    a		:   str	  =	m.group(2							)
    # Is it a weight or a bias?
    a		:   List[str]	  =	m.group(3							)

    # The name of the layer.
    a		:   Dict	  =	f"""transformer.h.{layer_idx}"""

    # For layernorm(s), simply store the layer norm.
    if op_name.endswith('layernorm'							):
      a		:   int	  =	'ln_1' if op_name.startswith('input'							) else 'ln_2'
      a		:   Optional[int]	  =	val

    # Transpose the QKV matrix.
    elif (
        op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
    ) and weight_or_bias == "weight":
      # Insert a tensor of 1x1xDxD bias.
      a		:   Optional[Any]	  =	torch.tril(torch.ones((n_positions, n_positions)					,     dtype=torch.floataa							)							).view(
          1					,     1					,     _A					,     _A							)
      a		:   List[Any]	  =	causal_mask

      # Insert a "dummy" tensor for masked_bias.
      a		:   Optional[Any]	  =	torch.tensor(-1E4					,     dtype=torch.floataa							)
      a		:   Dict	  =	masked_bias

      a		:   Dict	  =	fix_query_key_value_ordering(_A					,     _A					,     3					,     _A					,     _A							)
      # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
      a		:   Any	  =	out_val.transpose(0					,     1							).contiguous()
      # Store.
      a		:   Optional[int]	  =	out_val

    # Transpose the bias.
    elif (
        op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
    ) and weight_or_bias == "bias":
      a		:   List[str]	  =	fix_query_key_value_ordering(_A					,     _A					,     3					,     _A					,     _A							)
      # Store. No change of shape.
      a		:   List[str]	  =	out_val

    # Transpose the weights.
    elif weight_or_bias == "weight":
      a		:   Tuple	  =	megatron_to_transformers[op_name]
      a		:   Optional[int]	  =	val.transpose(0					,     1							)

    # Copy the bias.
    elif weight_or_bias == "bias":
      a		:   Dict	  =	megatron_to_transformers[op_name]
      a		:   int	  =	val

    # DEBUG.
  assert config.n_layer == layer_idx + 1

  # The final layernorm.
  a		:   Any	  =	transformer['final_layernorm.weight']
  a		:   Optional[Any]	  =	transformer['final_layernorm.bias']

  # For LM head, transformers' wants the matrix to weight embeddings.
  a		:   str	  =	word_embeddings

  # It should be done!
  return output_state_dict
def  lowerCamelCase__			(		):
  # Create the argument parser.
  a		:   str	  =	argparse.ArgumentParser()
  parser.add_argument('--print-checkpoint-structure'					,     action='store_true'							)
  parser.add_argument(
      'path_to_checkpoint'					,     type=_A					,     help='Path to the checkpoint file (.zip archive or direct .pt file)'					,     )
  parser.add_argument(
      '--config_file'					,     default=''					,     type=_A					,     help='An optional config json file describing the pre-trained model.'					,     )
  a		:   str	  =	parser.parse_args()

  # Extract the basename.
  a		:   str	  =	os.path.dirname(args.path_to_checkpoint							)

  # Load the model.
  # the .zip is very optional, let's keep it for backward compatibility
  print(f"""Extracting PyTorch state dictionary from {args.path_to_checkpoint}"""							)
  if args.path_to_checkpoint.endswith('.zip'							):
    with zipfile.ZipFile(args.path_to_checkpoint					,     'r'							) as checkpoint:
      with checkpoint.open('release/mp_rank_00/model_optim_rng.pt'							) as pytorch_dict:
        a		:   int	  =	torch.load(_A					,     map_location='cpu'							)
  else:
    a		:   Dict	  =	torch.load(args.path_to_checkpoint					,     map_location='cpu'							)

  a		:   Optional[Any]	  =	input_state_dict.get('args'					,     _A							)

  # Read the config, or default to the model released by NVIDIA.
  if args.config_file == "":
    if ds_args is not None:
      if ds_args.bias_gelu_fusion:
        a		:   List[Any]	  =	'gelu_fast'
      elif ds_args.openai_gelu:
        a		:   str	  =	'gelu_new'
      else:
        a		:   List[Any]	  =	'gelu'
    else:
      # in the very early days this used to be "gelu_new"
      a		:   str	  =	'gelu_new'

    # Spell out all parameters in case the defaults change.
    a		:   List[str]	  =	GPTaConfig(
        vocab_size=5_0257					,     n_positions=1024					,     n_embd=1024					,     n_layer=24					,     n_head=16					,     n_inner=4096					,     activation_function=_A					,     resid_pdrop=0.1					,     embd_pdrop=0.1					,     attn_pdrop=0.1					,     layer_norm_epsilon=1E-5					,     initializer_range=0.02					,     summary_type='cls_index'					,     summary_use_proj=_A					,     summary_activation=_A					,     summary_proj_to_labels=_A					,     summary_first_dropout=0.1					,     scale_attn_weights=_A					,     use_cache=_A					,     bos_token_id=5_0256					,     eos_token_id=5_0256					,     )
  else:
    a		:   Optional[int]	  =	GPTaConfig.from_json_file(args.config_file							)

  a		:   Union[str, Any]	  =	['GPT2LMHeadModel']

  # Convert.
  print('Converting'							)
  a		:   List[Any]	  =	convert_megatron_checkpoint(_A					,     _A					,     _A							)

  # Print the structure of converted state dict.
  if args.print_checkpoint_structure:
    recursive_print(_A					,     _A							)

  # Add tokenizer class info to config
  # see https://github.com/huggingface/transformers/issues/13906)
  if ds_args is not None:
    a		:   Union[str, Any]	  =	ds_args.tokenizer_type
    if tokenizer_type == "GPT2BPETokenizer":
      a		:   Dict	  =	'gpt2'
    elif tokenizer_type == "PretrainedFromHF":
      a		:   Optional[int]	  =	ds_args.tokenizer_name_or_path
    else:
      raise ValueError(f"""Unrecognized tokenizer_type {tokenizer_type}"""							)
  else:
    a		:   int	  =	'gpt2'

  a		:   Tuple	  =	AutoTokenizer.from_pretrained(_A							)
  a		:   Any	  =	type(_A							).__name__
  a		:   int	  =	tokenizer_class

  # Store the config to file.
  print('Saving config'							)
  config.save_pretrained(_A							)

  # Save tokenizer based on args
  print(f"""Adding {tokenizer_class} tokenizer files"""							)
  tokenizer.save_pretrained(_A							)

  # Store the state_dict to file.
  a		:   Any	  =	os.path.join(_A					,     'pytorch_model.bin'							)
  print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""							)
  torch.save(_A					,     _A							)


####################################################################################################

if __name__ == "__main__":
     main()

####################################################################################################




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase:   Dict					=		{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   Optional[int]					=		[
         'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ViTMAEForPreTraining',
         'ViTMAELayer',
         'ViTMAEModel',
         'ViTMAEPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   List[Any]					=		[
         'TFViTMAEForPreTraining',
         'TFViTMAEModel',
         'TFViTMAEPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_vit_mae import (
              VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
              ViTMAEForPreTraining,
              ViTMAELayer,
              ViTMAEModel,
              ViTMAEPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
     import sys

     lowerCAmelCase:   Tuple					=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)